Dev tools · alternatives · 2026

24 alternatives to Fast Deep Reinforcement Learning Course

I worked on this applied Deep Reinforcement Learning course for the better part of 2021. I made a Datacamp course [0] before, and this served as my inspiration to make an applied Deep RL series. Normally, Deep RL courses teach a lot of mathematically involved theory. You get the practical applications near the end (if at all). I have tried to turn that on its head. In the top-down approach, you learn practical skills first, then go deeper later. This is much more fun. This course (the first in a planned multi-part series) shows how to use the Deep Reinforcement Learning framework RLlib to…

Fast Deep Reinforcement Learning Course is an educational series that teaches applied deep reinforcement learning through a practical, top-down approach. Students learn to use the RLlib framework to solve OpenAI Gym… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Fast Deep Reinforcement Learning Course launched in 2022; newer entries below may have overtaken it.

1
I RL-trained an agent that trains models with RL (for ~$1.3k)▲107
RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.
Jul 2026 · github.com · its alternatives →
2
Comprehensive Tutorials in Deep Learning Using TensorFlow▲327
:satellite: Simple and ready-to-use tutorials for TensorFlow - GitHub - instillai/TensorFlow-Course: :satellite: Simple and ready-to-use tutorials for TensorFlow
2018 · github.com · its alternatives →
3
Multi-GPU Reinforcement Learning in Tensorflow for OpenAI Gym▲58
Multi-GPU reinforcement learning using Deep Q-Network in TensorFlow for OpenAI Gym - vishar0/dist-dqn
2016 · github.com · its alternatives →
4
DeepSeek R1▲322
Advanced reasoning model
2025 · github.com · its alternatives →
5
ART – a new open-source RL framework for training agents▲116
Hey HN, I wanted to share a new project we've been working on for the last couple of months called ART (https://github.com/OpenPipe/ART). ART is a new open-source framework for training agents using reinforcement learning (RL). RL allows you to train an agent to perform better at any task whose outcome can be measured and quantified. There are many excellent projects focused on training LLMs with RL, such as GRPOTrainer (https://huggingface.co/docs/trl/main/en/grpo_trainer) and verl…
2025 · github.com · its alternatives →
6
Simple and comprehensive tutorials in Deep Learning using TensorFlow▲157
Simple and comprehensive tutorials in TensorFlow. Contribute to easy-tensorflow/easy-tensorflow development by creating an account on GitHub.
2018 · github.com · its alternatives →
7
I trained a deep learning model to build sites for me▲397
2019 · zecoda.com · its alternatives →
8
I built a game to help you learn neural network architectures▲321
2024 · graphgame.sabrina.dev · its alternatives →
9
A Course on Deep Learning Using Google's TensorFlow▲39
:earth_americas: Simple and ready-to-use tutorials for TensorFlow - astorfi/TensorFlow-World
2019 · github.com · its alternatives →
10
Collection of deep learning implementations with side-by-side notes▲251
2021 · nn.labml.ai · its alternatives →
11
Deep Learning in TensorFlow – The Roadmap for Study and Learning▲146
:satellite: Organized & Useful Resources about Deep Learning with TensorFlow - astorfi/TensorFlow-World-Resources
2019 · github.com · its alternatives →
12IC
I co-wrote a book on training Deep Learning models in Go▲45
A friend and I wrote a book on how to build and train Deep Learning models in Go. We wanted it to be a useful reference for deep learning basics for Go programmers. Deep Learning is slowly seeping into everything we use every day and we thought it would be great if more people could do it in Go. The book is available here and on Amazon as well. https://www.packtpub.com/big-data-and-business-intelligence/hands-deep-learning-go We would appreciate any feedback and we're always looking to improve.
2019 · its alternatives →
13
OpenAI Gym▲147
Open source interface to reinforcement learning tasks
2016 · its alternatives →
14
Simple Deep Learning Tutorials using Microsoft Cognitive Toolkit▲126
:earth_americas: Simple and ready-to-use deep learning examples for the Microsoft Cognitive Toolkit (CNTK) - CNTK-World/README.rst at master · astorfi/CNTK-World
2017 · github.com · its alternatives →
15
Llama-8B Teaches Itself Baby Steps to Deep Research Using RL▲39
I've been tinkering with getting Llama-8B to bootstrap its own research skills through self-play. The model generates questions about documents, searches for answers, and then learns from its own successes/failures through RL (hacked up Unsloth's GRPO code). Started with just 23% accuracy on Apollo 13 mission report questions and hit 53% after less than an hour of training. Everything runs locally using open-source models. It's cool to see the model go from completely botching search queries to iteratively researching to get the right answer.
2025 · github.com · its alternatives →
16
Terminal-Bench-RL: Training long-horizon terminal agents with RL▲125
After training calculator agent via RL, I really wanted to go bigger! So I built RL infrastructure for training long-horizon terminal/coding agents that scales from 2x A100s to 32x H100s (~$1M worth of compute!) Without any training, my 32B agent hit #19 on Terminal-Bench leaderboard, beating Stanford's Terminus-Qwen3-235B-A22! With training... well, too expensive, but I bet the results would be good! *What I did*: - Created a Claude Code-inspired agent (system msg + tools) - Built Docker-isolated GRPO training where each rollout gets its own container - Developed a multi-agent…
2025 · github.com · its alternatives →
17
A Comprehensive Free Course in Deep Learning Using TensorFlow▲19
:earth_americas: Simple and ready-to-use tutorials for TensorFlow - astorfi/TensorFlow-World
2018 · github.com · its alternatives →
18
Natural Language Processing Demystified (Part One)▲166
Hi HN: I published part one of my free NLP course. The course is intended to help anyone who knows Python and a bit of math go from the very basics all the way to today's mainstream models and frameworks. I strive to balance theory and practice and so every module consists of detailed explanations and slides along with a Colab notebook (in most modules) putting the theory into practice. In part one, we cover text preprocessing, how to turn text into numbers, and multiple ways to classify and search text using "classical" approaches. And along the way, we'll pick up useful bits on how to use…
2022 · nlpdemystified.org · its alternatives →
19AI
An Interactive Guide to Teach Derivatives and Backpropagation▲119
2023 · x0axz.com · its alternatives →
20
I built a deep learning engine from scratch in Python▲30
I’ve spent the last few months building a deep learning engine completely from scratch in Python (using only math and random). What started as a basic linear algebra calculator project grew into a symbolic tensor system with autodiff, custom matrix ops, attention mechanisms, LayerNorm, GELU, and even a text generation demo trained on the Brown corpus. I'm still an undergrad, so my main goal is to deeply understand how deep learning actually works under the hood - gradients, attention, backpropagation, optimizers - by building it step-by-step with full visibility into everything, and without…
2025 · github.com · its alternatives →
21
Neural networks tutorial series with code▲107
The simplest form of an artificial neural network explained and demonstrated. - GitHub - gokadin/ai-simplest-network: The simplest form of an artificial neural network explained and demonstrated.
2020 · github.com · its alternatives →
22
Deep Learning Using TensorFlow – Tutorials and Source Code▲31
:earth_americas: Simple and ready-to-use tutorials for TensorFlow - TensorFlow-World/README.rst at master · astorfi/TensorFlow-World
2017 · github.com · its alternatives →
23
Eclipse Deeplearning4j▲73
Deep learning is a fast-growing field within artificial intelligence, and deep neural networks have seen rapid adoption in recent years. Successes in machine vision, speech recognition and natural language processing show the technology is ripe for adoption by enterprise. Eclipse Deeplearning4j targ
2017 · projects.eclipse.org · its alternatives →
24
Deep-ML▲13
We just launched Deep-ML Labs, a new section on Deep-ML Each Lab is a hands-on challenge where you implement parts of real models, activations, layers, optimizers, normalization, even small transformers — directly in PyTorch (and sometimes NumPy). You can test your implementations on actual datasets, see how your results compare to others, and understand what’s really happening under the hood of deep learning frameworks. let me know what you guys think and if we should work on making more of these questions
2025 · deep-ml.com · its alternatives →

Also compare

Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →